def Find_Lowest_Energy_Structure_Electrostatics(self):
"""
Find oxidized/reduced combination with lowest electrostatic energy
"""
n_Na = self.structure.composition['Na']
n_S = self.structure.composition['S']
n_O = self.structure.composition['O']
n_N = self.structure.composition['N']
n_Fe = self.structure.composition['Fe']
n_Fe_reduced = self.variable_magnetization_dict['Fe']['n_reduced']
n_Fe_oxidized = n_Fe-n_Fe_reduced
N_charge = ( 2.*n_O-6.*n_S-n_Na-3.*n_Fe_oxidized-2.*n_Fe_reduced )/n_N
oxidation_states = {'Na':+1, 'Fe':+3, 'O':-2,'S':+6,'N':N_charge}
Fe_2plus = pymatgen.Specie('Fe',oxidation_state=+2)
structure_with_charges = self.structure.copy()
structure_with_charges.add_oxidation_state_by_element(oxidation_states)
# identify Fe sites
list_Fe_indices = []
for i,site in enumerate(structure_with_charges):
if site.specie.symbol == 'Fe':
list_Fe_indices.append(i)
# Generate all possible permutation of sites and compute
# Ewald energy
ewald_model = EwaldElectrostaticModel(acc_factor=6)
list_reduced_sets = []
list_ewald_energy = []
for reduced_set in itertools.combinations(list_Fe_indices,n_Fe_reduced):
list_reduced_sets.append(reduced_set)
struct = structure_with_charges.copy()
for i in reduced_set:
struct.replace(i, Fe_2plus)
list_ewald_energy.append(ewald_model.get_energy(struct))
if len(list_ewald_energy) == 0:
# all sites are oxidized. No sorting involved
list_reduced_site_indices = []
list_oxidized_site_indices = list_Fe_indices
else:
# some reduction takes place. Identify best electrostatic choice
imin = np.argmin(list_ewald_energy)
list_reduced_site_indices = list_reduced_sets[imin]
list_oxidized_site_indices = []
for i in list_Fe_indices:
if i not in list_reduced_site_indices:
list_oxidized_site_indices.append(i)
return list_reduced_site_indices, list_oxidized_site_indices
import pymatgen as mg
si = mg.Element("Si")
print("Atomic mass of Si is {}".format(si.atomic_mass))
print("Si has a melting point of {}".format(si.melting_point))
print("Ionic radii for Si: {}".format(si.ionic_radii))
print("Atomic mass of Si in kg: {}".format(si.atomic_mass.to("kg")))
fe2 = mg.Specie("Fe", 2)
print(fe2.atomic_mass)
print(fe2.ionic_radius)
comp = mg.Composition("Fe2O3")
print("Weight of Fe2O3 is {}".format(comp.weight))
print("Amount of Fe in Fe2O3 is {}".format(comp["Fe"]))
print("Atomic fraction of Fe is {}".format(comp.get_atomic_fraction("Fe")))
print("Weight fraction of Fe is {}".format(comp.get_wt_fraction("Fe")))
# Creates cubic Lattice with lattice parameter 4.2
lattice = mg.Lattice.cubic(4.2)
print(lattice.lengths_and_angles)
structure = mg.Structure(lattice, ["Cs", "Cl"], [[0, 0, 0], [0.5, 0.5, 0.5]])
print("Unit cell vol = {}".format(structure.volume))
print("First site of the structure is {}".format(structure[0]))
structure.make_supercell([2, 2,
1]) #Make a 3 x 2 x 1 supercell of the structure
del structure[0] #Remove the first site
structure.append("Na", [0, 0, 0]) #Append a Na atom.
def _modify_structure(self):
# We'll use "oxidation_state" as a stand-in for "magnetisation"
# surrounded by nitrogen
self.Fe_HS_2plus = pymatgen.Specie('Fe',oxidation_state=4)
self.Fe_HS_3plus = pymatgen.Specie('Fe',oxidation_state=5)
# surrounded by carbon
self.Fe_LS_2plus = pymatgen.Specie('Fe',oxidation_state=0)
self.Fe_LS_3plus = pymatgen.Specie('Fe',oxidation_state=1)
Fe = pymatgen.Element('Fe')
Na = pymatgen.Element('Na')
N = pymatgen.Element('N')
C = pymatgen.Element('C')
Na_indices = self.structure.indices_from_symbol('Na')
neibhor_distance = 2.6 # angstrom
# Interrogate the structure: for each Fe, what is the site index, the nearest neighbor
# and the distance to Na?
# create a nice verbose object for this job
Iron_Ion = namedtuple('Iron_Ion',['structure_index', 'neighbor', 'distance_to_Na'])
# Count the number of reducing electrons which must be introduced
number_of_reducing_electrons = self.structure.composition['Na']
list_Fe_ions = []
# Identify all the Fe ions in the structure
for i,site in enumerate(self.structure.sites):
if site.specie == Fe:
neighbor = self.structure.get_neighbors(site,neibhor_distance)[0][0].specie
if number_of_reducing_electrons > 0:
distance = np.min(self.structure.distance_matrix[i,Na_indices])
else:
distance = np.infty
list_Fe_ions.append( Iron_Ion(i,neighbor,distance) )
# Spoof pymatgen by substituting reduced iron for the plain vanilla Fe.
while number_of_reducing_electrons > 0:
next_ion = self.find_next_site_to_reduce_pop(list_Fe_ions)
if next_ion.neighbor == N:
self.structure.replace(next_ion.structure_index,self.Fe_HS_2plus )
elif next_ion.neighbor == C:
self.structure.replace(next_ion.structure_index,self.Fe_LS_2plus )
number_of_reducing_electrons -= 1
# Spoof pymatgen by substituting oxidized iron for the plain vanilla Fe for the remaining sites
for next_ion in list_Fe_ions:
if next_ion.neighbor == N:
self.structure.replace(next_ion.structure_index,self.Fe_HS_3plus )
elif next_ion.neighbor == C:
self.structure.replace(next_ion.structure_index,self.Fe_LS_3plus )
# Sort structure, so that decorated sites are
# next to each other
self.structure.sort()
self.structure_has_been_modified = True
return